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https://github.com/openvinotoolkit/stable-diffusion-webui.git
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Transition to using settings through UI instead of cmd line args. Added feature to only apply to hr-fix. Install package using requirements_versions.txt
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@ -280,9 +280,6 @@ def prepare_environment():
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elif platform.system() == "Linux":
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elif platform.system() == "Linux":
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run_pip(f"install {xformers_package}", "xformers")
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run_pip(f"install {xformers_package}", "xformers")
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if not is_installed("tomesd") and args.token_merging:
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run_pip(f"install tomesd")
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if not is_installed("pyngrok") and args.ngrok:
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if not is_installed("pyngrok") and args.ngrok:
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run_pip("install pyngrok", "ngrok")
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run_pip("install pyngrok", "ngrok")
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@ -29,6 +29,7 @@ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
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from einops import repeat, rearrange
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from einops import repeat, rearrange
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from blendmodes.blend import blendLayers, BlendType
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from blendmodes.blend import blendLayers, BlendType
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import tomesd
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# some of those options should not be changed at all because they would break the model, so I removed them from options.
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# some of those options should not be changed at all because they would break the model, so I removed them from options.
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opt_C = 4
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opt_C = 4
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@ -500,9 +501,28 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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if k == 'sd_vae':
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if k == 'sd_vae':
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sd_vae.reload_vae_weights()
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sd_vae.reload_vae_weights()
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if opts.token_merging:
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if p.hr_second_pass_steps < 1 and not opts.token_merging_hr_only:
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tomesd.apply_patch(
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p.sd_model,
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ratio=opts.token_merging_ratio,
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max_downsample=opts.token_merging_maximum_down_sampling,
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sx=opts.token_merging_stride_x,
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sy=opts.token_merging_stride_y,
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use_rand=opts.token_merging_random,
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merge_attn=opts.token_merging_merge_attention,
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merge_crossattn=opts.token_merging_merge_cross_attention,
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merge_mlp=opts.token_merging_merge_mlp
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)
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res = process_images_inner(p)
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res = process_images_inner(p)
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finally:
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finally:
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# undo model optimizations made by tomesd
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if opts.token_merging:
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tomesd.remove_patch(p.sd_model)
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# restore opts to original state
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# restore opts to original state
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if p.override_settings_restore_afterwards:
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if p.override_settings_restore_afterwards:
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for k, v in stored_opts.items():
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for k, v in stored_opts.items():
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@ -938,6 +958,21 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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x = None
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x = None
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devices.torch_gc()
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devices.torch_gc()
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# apply token merging optimizations from tomesd for high-res pass
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# check if hr_only so we don't redundantly apply patch
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if opts.token_merging and opts.token_merging_hr_only:
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tomesd.apply_patch(
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self.sd_model,
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ratio=opts.token_merging_ratio,
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max_downsample=opts.token_merging_maximum_down_sampling,
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sx=opts.token_merging_stride_x,
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sy=opts.token_merging_stride_y,
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use_rand=opts.token_merging_random,
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merge_attn=opts.token_merging_merge_attention,
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merge_crossattn=opts.token_merging_merge_cross_attention,
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merge_mlp=opts.token_merging_merge_mlp
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)
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samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
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samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
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return samples
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return samples
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@ -431,13 +431,6 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_
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with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
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with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
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sd_model = instantiate_from_config(sd_config.model)
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sd_model = instantiate_from_config(sd_config.model)
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if shared.cmd_opts.token_merging:
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import tomesd
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ratio = shared.cmd_opts.token_merging_ratio
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tomesd.apply_patch(sd_model, ratio=ratio)
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print(f"Model accelerated using {(ratio * 100)}% token merging via tomesd.")
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timer.record("token merging")
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except Exception as e:
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except Exception as e:
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pass
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pass
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@ -427,6 +427,50 @@ options_templates.update(options_section((None, "Hidden options"), {
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"sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
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"sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
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}))
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}))
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options_templates.update(options_section(('token_merging', 'Token Merging'), {
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"token_merging": OptionInfo(
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False, "Enable redundant token merging via tomesd. (currently incompatible with controlnet extension)",
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gr.Checkbox
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),
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"token_merging_ratio": OptionInfo(
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0.5, "Merging Ratio",
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gr.Slider, {"minimum": 0, "maximum": 0.9, "step": 0.1}
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),
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"token_merging_hr_only": OptionInfo(
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True, "Apply only to high-res fix pass. Disabling can yield a ~20-35% speedup on contemporary resolutions.",
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gr.Checkbox
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),
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# More advanced/niche settings:
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"token_merging_random": OptionInfo(
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True, "Use random perturbations - Disabling might help with certain samplers",
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gr.Checkbox
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),
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"token_merging_merge_attention": OptionInfo(
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True, "Merge attention",
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gr.Checkbox
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),
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"token_merging_merge_cross_attention": OptionInfo(
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False, "Merge cross attention",
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gr.Checkbox
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),
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"token_merging_merge_mlp": OptionInfo(
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False, "Merge mlp",
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gr.Checkbox
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),
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"token_merging_maximum_down_sampling": OptionInfo(
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1, "Maximum down sampling",
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gr.Dropdown, lambda: {"choices": ["1", "2", "4", "8"]}
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),
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"token_merging_stride_x": OptionInfo(
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2, "Stride - X",
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gr.Slider, {"minimum": 2, "maximum": 8, "step": 2}
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),
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"token_merging_stride_y": OptionInfo(
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2, "Stride - Y",
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gr.Slider, {"minimum": 2, "maximum": 8, "step": 2}
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)
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}))
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options_templates.update()
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options_templates.update()
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@ -28,3 +28,4 @@ torchsde==0.2.5
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safetensors==0.3.0
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safetensors==0.3.0
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httpcore<=0.15
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httpcore<=0.15
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fastapi==0.94.0
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fastapi==0.94.0
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tomesd>=0.1
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